![]() In: International Conference on Recent Developments in Science, Engineering and Technology. ![]() A database for handwritten yoruba characters. Īdubi SA, Misra S (2016) Syllable-based text compression: a language case study. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy. Accessed 24 July 2020Īgić Z, Vulić I (2019) JW300: A wide-coverage parallel corpus for low-resource languages. In: Proceedings of the Second Workshop on Statistical Machine Translation, Prague, Czech Republic. Lavie A, Agarwal A (2020) METEOR: an automatic metric for MT evaluation with high levels of correlation with human judgments. Lavie A, Sagae K, Jayaraman S (2004) The significance of recall in automatic metrics for MT evaluation, vol. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania, USA. Papineni K, Roukos S, Ward T, Zhu W-J (2002) Bleu: a method for automatic evaluation of machine translation. Īhia O, Ogueji K (2020) Towards supervised and unsupervised neural machine translation baselines for nigerian pidgin. In: Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium. Lample G, Ott M, Conneau A, Denoyer L, Ranzato M (2018) Phrase-based & neural unsupervised machine translation. Ogueji K, Ahia O (2019) PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English In: Huang X, Jiang J, Zhao D, Feng Y, Hong Y (eds) Natural Language Processing and Chinese Computing. Zhou L, Zhang J, Zong C (2018) Look-ahead attention for generation in neural machine translation. ArXiv E-Prints, ArXiv:1409.3215Ĭho K, van Merrienboer B, Gülçehre C, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder–decoder for statistical machine translation. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. Mikolov T, Le QV, Sutskever I (2020) Exploiting similarities among languages for machine translation. Sánchez-Martínez F, Forcada ML (2009) Inferring shallow-transfer machine translation rules from small parallel corpora. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, pp 12–21. Ravi S, Knight K (2011) Deciphering Foreign Language. M.S Thesis, Comp Sci, IIIT, Hyderabad, India. Karan S (2015) Methods for leveraging lexical information in SMT. Accessed Īrtetxe M, Labaka G, Agirre E (2020) Unsupervised statistical machine translation. In: Proceedings of the 2003 human language technology conference of the north american chapter of the association for computational linguistics. Koehn P, Och FJ, Marcu D (2003) Statistical phrase-based translation. Marcu D, Wong W (2002) A phrase-based, joint probability model for statistical machine Translation Daniel Marcu. īrown PF et al (1990) A statistical approach to machine translation. Lopez A (2007) A survey of statistical machine translation. Available: Nigeria mobile internet user penetration 2025 | Statista. Mobile internet user penetration in Nigeria from 2015 to 2025”. Keywordsībc starts pidgin digital service for west Africa audiences (2017). Studies that look at in-depth pre-translation strategies for developing translation machine model are green areas for pidgin-English translation. This indicates that the accuracy is dependent on the level and type of hybrid used. From our findings, our hybrid model outperforms the baseline NMT model with a BLEU score of 1.05 on two-level translation. The Bi-Lingual Evaluation Understudy (BLEU) score was employed as a metric of measurement. From the JW300 public dataset, we used 22,047 sentence pairs for training our model,1000 for tuning, and 2520 for testing. In this paper, we propose a hybrid-strategic model that improves the accuracy of the baseline Neural Machine Translation Model (NMT) in translating pidgin English to the English language. To proffer a solution, researchers in machine translation from Pidgin English to the English language have leveraged only unsupervised and supervised Neural Machine Translation-based models. With the development in web technology and the English language dominancy of web content, this growing population stands disadvantaged in understanding content on the web. Despite the diversity, one common point of unification, especially among the West African communities is the spoken pidgin-English language. The African continent is made up of people with rich diverse cultures and spoken languages.
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